Dynamic allocation of inference-time compute across design conditions—rather than fixed upfront choices—dramatically improves sample efficiency in molecular design by steering toward high-yield configurations on the fly.
This paper tackles de novo nanobody design by treating condition selection (like scaffold choice and target epitope) as a dynamic resource-allocation problem during inference.